---
_id: '25278'
abstract:
- lang: eng
text: Using Service Function Chaining (SFC) in wireless networks became popular
in many domains like networking and multimedia. It relies on allocating network
resources to incoming SFCs requests, via a Virtual Network Embedding (VNE) algorithm,
so that it optimizes the performance of the SFC. When the load of incoming requests
-- competing for the limited network resources -- increases, it becomes challenging
to decide which requests should be admitted and which one should be rejected.
In this work, we propose a deep Reinforcement learning (RL) solution that can
learn the admission policy for different dependencies, such as the service lifetime
and the priority of incoming requests. We compare the deep RL solution to a first-come-first-serve
baseline that admits a request whenever there are available resources. We show
that deep RL outperforms the baseline and provides higher acceptance rate with
low rejections even when there are enough resources.
author:
- first_name: Haitham
full_name: Afifi, Haitham
id: '65718'
last_name: Afifi
- first_name: Fabian Jakob
full_name: Sauer, Fabian Jakob
last_name: Sauer
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Afifi H, Sauer FJ, Karl H. Reinforcement Learning for Admission Control in
Wireless Virtual Network Embedding. In: 2021 IEEE International Conference
on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21). ; 2021.'
apa: Afifi, H., Sauer, F. J., & Karl, H. (2021). Reinforcement Learning for
Admission Control in Wireless Virtual Network Embedding. 2021 IEEE International
Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21).
bibtex: '@inproceedings{Afifi_Sauer_Karl_2021, place={Hyderabad, India}, title={Reinforcement
Learning for Admission Control in Wireless Virtual Network Embedding}, booktitle={2021
IEEE International Conference on Advanced Networks and Telecommunications Systems
(ANTS) (ANTS’21)}, author={Afifi, Haitham and Sauer, Fabian Jakob and Karl, Holger},
year={2021} }'
chicago: Afifi, Haitham, Fabian Jakob Sauer, and Holger Karl. “Reinforcement Learning
for Admission Control in Wireless Virtual Network Embedding.” In 2021 IEEE
International Conference on Advanced Networks and Telecommunications Systems (ANTS)
(ANTS’21). Hyderabad, India, 2021.
ieee: H. Afifi, F. J. Sauer, and H. Karl, “Reinforcement Learning for Admission
Control in Wireless Virtual Network Embedding,” 2021.
mla: Afifi, Haitham, et al. “Reinforcement Learning for Admission Control in Wireless
Virtual Network Embedding.” 2021 IEEE International Conference on Advanced
Networks and Telecommunications Systems (ANTS) (ANTS’21), 2021.
short: 'H. Afifi, F.J. Sauer, H. Karl, in: 2021 IEEE International Conference on
Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21), Hyderabad,
India, 2021.'
date_created: 2021-10-04T10:42:20Z
date_updated: 2022-01-06T06:56:58Z
ddc:
- '000'
file:
- access_level: closed
content_type: application/pdf
creator: hafifi
date_created: 2021-10-04T10:43:19Z
date_updated: 2021-10-04T10:43:19Z
file_id: '25279'
file_name: Preprint___Reinforcement_Learning_for_Dynamic_Resource_Allocation_in_Wireless_Networks.pdf
file_size: 534737
relation: main_file
success: 1
file_date_updated: 2021-10-04T10:43:19Z
has_accepted_license: '1'
keyword:
- reinforcement learning
- admission control
- wireless sensor networks
language:
- iso: eng
place: Hyderabad, India
project:
- _id: '27'
name: Akustische Sensornetzwerke - Teilprojekt "Verteilte akustische Signalverarbeitung
über funkbasierte Sensornetzwerke
publication: 2021 IEEE International Conference on Advanced Networks and Telecommunications
Systems (ANTS) (ANTS'21)
status: public
title: Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding
type: conference
user_id: '65718'
year: '2021'
...
---
_id: '25281'
abstract:
- lang: eng
text: "Wireless Acoustic Sensor Networks (WASNs) have a wide range of audio signal
processing applications. Due to the spatial diversity of the microphone and their
relative position to the acoustic source, not all microphones are equally useful
for subsequent audio signal processing tasks, nor do they all have the same wireless
data transmission rates. Hence, a central task in WASNs is to balance a microphone’s
estimated acoustic utility against its transmission delay, selecting a best-possible
subset of microphones to record audio signals.\r\n\r\nIn this work, we use reinforcement
learning to decide if a microphone should be used or switched off to maximize
the acoustic quality at low transmission delays, while minimizing switching frequency.
In experiments with moving sources in a simulated acoustic environment, our method
outperforms naive baseline comparisons"
author:
- first_name: Haitham
full_name: Afifi, Haitham
id: '65718'
last_name: Afifi
- first_name: Michael
full_name: Guenther, Michael
last_name: Guenther
- first_name: Andreas
full_name: Brendel, Andreas
last_name: Brendel
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
- first_name: Walter
full_name: Kellermann, Walter
last_name: Kellermann
citation:
ama: 'Afifi H, Guenther M, Brendel A, Karl H, Kellermann W. Reinforcement Learning-based
Microphone Selection in Wireless Acoustic Sensor Networks considering Network
and Acoustic Utilities. In: 14. ITG Conference on Speech Communication (ITG
2021). ; 2021.'
apa: Afifi, H., Guenther, M., Brendel, A., Karl, H., & Kellermann, W. (2021).
Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor
Networks considering Network and Acoustic Utilities. 14. ITG Conference on
Speech Communication (ITG 2021).
bibtex: '@inproceedings{Afifi_Guenther_Brendel_Karl_Kellermann_2021, title={Reinforcement
Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering
Network and Acoustic Utilities}, booktitle={14. ITG Conference on Speech Communication
(ITG 2021)}, author={Afifi, Haitham and Guenther, Michael and Brendel, Andreas
and Karl, Holger and Kellermann, Walter}, year={2021} }'
chicago: Afifi, Haitham, Michael Guenther, Andreas Brendel, Holger Karl, and Walter
Kellermann. “Reinforcement Learning-Based Microphone Selection in Wireless Acoustic
Sensor Networks Considering Network and Acoustic Utilities.” In 14. ITG Conference
on Speech Communication (ITG 2021), 2021.
ieee: H. Afifi, M. Guenther, A. Brendel, H. Karl, and W. Kellermann, “Reinforcement
Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering
Network and Acoustic Utilities,” 2021.
mla: Afifi, Haitham, et al. “Reinforcement Learning-Based Microphone Selection in
Wireless Acoustic Sensor Networks Considering Network and Acoustic Utilities.”
14. ITG Conference on Speech Communication (ITG 2021), 2021.
short: 'H. Afifi, M. Guenther, A. Brendel, H. Karl, W. Kellermann, in: 14. ITG Conference
on Speech Communication (ITG 2021), 2021.'
date_created: 2021-10-04T10:59:50Z
date_updated: 2022-01-06T06:56:59Z
ddc:
- '620'
file:
- access_level: closed
content_type: application/pdf
creator: hafifi
date_created: 2021-10-04T10:58:07Z
date_updated: 2021-10-04T10:58:07Z
file_id: '25282'
file_name: ITG_2021_paper_26 (3).pdf
file_size: 283616
relation: main_file
success: 1
file_date_updated: 2021-10-04T10:58:07Z
has_accepted_license: '1'
keyword:
- microphone utility
- microphone selection
- wireless acoustic sensor network
- network delay
- reinforcement learning
language:
- iso: eng
project:
- _id: '27'
name: Akustische Sensornetzwerke - Teilprojekt "Verteilte akustische Signalverarbeitung
über funkbasierte Sensornetzwerke
publication: 14. ITG Conference on Speech Communication (ITG 2021)
status: public
title: Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor
Networks considering Network and Acoustic Utilities
type: conference
user_id: '65718'
year: '2021'
...
---
_id: '20125'
abstract:
- lang: eng
text: Datacenter applications have different resource requirements from network
and developing flow scheduling heuristics for every workload is practically infeasible.
In this paper, we show that deep reinforcement learning (RL) can be used to efficiently
learn flow scheduling policies for different workloads without manual feature
engineering. Specifically, we present LFS, which learns to optimize a high-level
performance objective, e.g., maximize the number of flow admissions while meeting
the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling
policy on continuous online flow arrivals. The evaluation results show that the
trained LFS scheduler admits 1.05x more flows than the greedy flow scheduling
heuristics under varying network load.
author:
- first_name: Asif
full_name: Hasnain, Asif
id: '63288'
last_name: Hasnain
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Hasnain A, Karl H. Learning Flow Scheduling. In: 2021 IEEE 18th Annual
Consumer Communications & Networking Conference (CCNC). IEEE Computer
Society. doi:https://doi.org/10.1109/CCNC49032.2021.9369514'
apa: 'Hasnain, A., & Karl, H. (n.d.). Learning Flow Scheduling. In 2021 IEEE
18th Annual Consumer Communications & Networking Conference (CCNC). Las
Vegas, USA: IEEE Computer Society. https://doi.org/10.1109/CCNC49032.2021.9369514'
bibtex: '@inproceedings{Hasnain_Karl, title={Learning Flow Scheduling}, DOI={https://doi.org/10.1109/CCNC49032.2021.9369514},
booktitle={2021 IEEE 18th Annual Consumer Communications & Networking Conference
(CCNC)}, publisher={IEEE Computer Society}, author={Hasnain, Asif and Karl, Holger}
}'
chicago: Hasnain, Asif, and Holger Karl. “Learning Flow Scheduling.” In 2021
IEEE 18th Annual Consumer Communications & Networking Conference (CCNC).
IEEE Computer Society, n.d. https://doi.org/10.1109/CCNC49032.2021.9369514.
ieee: A. Hasnain and H. Karl, “Learning Flow Scheduling,” in 2021 IEEE 18th Annual
Consumer Communications & Networking Conference (CCNC), Las Vegas, USA.
mla: Hasnain, Asif, and Holger Karl. “Learning Flow Scheduling.” 2021 IEEE 18th
Annual Consumer Communications & Networking Conference (CCNC), IEEE Computer
Society, doi:https://doi.org/10.1109/CCNC49032.2021.9369514.
short: 'A. Hasnain, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications &
Networking Conference (CCNC), IEEE Computer Society, n.d.'
conference:
end_date: 2021-01-12
location: Las Vegas, USA
name: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)
start_date: 2021-01-09
date_created: 2020-10-19T14:27:17Z
date_updated: 2022-01-06T06:54:20Z
ddc:
- '000'
department:
- _id: '75'
doi: https://doi.org/10.1109/CCNC49032.2021.9369514
keyword:
- Flow scheduling
- Deadlines
- Reinforcement learning
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9369514
project:
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
- _id: '1'
name: SFB 901
publication: 2021 IEEE 18th Annual Consumer Communications & Networking Conference
(CCNC)
publication_status: accepted
publisher: IEEE Computer Society
status: public
title: Learning Flow Scheduling
type: conference
user_id: '63288'
year: '2021'
...
---
_id: '28349'
abstract:
- lang: ger
text: "Das Auftreten der COVID-19-Pandemie stellt Fremdsprachenkurse vielerorts
vor Herausforderungen. Unter Zuhilfenahme diverser digitaler Tools werden nicht
nur Lernmaterialien online geteilt, sondern auch die Interaktion zwischen Lehrenden
und Lernenden sowie der Lernenden untereinander in den virtuellen Raum verlagert.
Qualitative Interviews mit den Beteiligten erfassen, wie diese mit den Herausforderungen
videogestützten Sprachunterrichts umgehen und welche Strategien sie wählen, um
Sprachenlernen zu ermöglichen. Die Ergebnisse zeigen auf, wo seitens der Kursorganisation
und -durchführung Handlungsbedarf besteht.\r\n-----\r\nThe rise of the COVID-19
pandemic challenges the teaching and learning of foreign languages at many institutions.
The implementation of various digital tools aids not only the online sharing of
learning materials, but also shifts teacher-learner and learner-learner interaction
to the virtual space. Via qualitative interviews, this study examines how both
teachers and learners handle the challenges of language instruction based on videoconferences,
and what strategies they employ to enable language learning. The results highlight
areas in need of improvement in terms of course organization and facilitation."
article_type: original
author:
- first_name: Sandra
full_name: Drumm, Sandra
last_name: Drumm
- first_name: Mareike
full_name: Müller, Mareike
id: '71540'
last_name: Müller
- first_name: Nadja
full_name: Stenzel, Nadja
last_name: Stenzel
citation:
ama: 'Drumm S, Müller M, Stenzel N. Digitale Räume geben und nehmen: Unterrichtsinteraktion
in DSH-Kursen während der COVID-19-Pandemie. Informationen Deutsch als Fremdsprache.
2021;48(5):496-515. doi:10.1515/infodaf-2021-0069'
apa: 'Drumm, S., Müller, M., & Stenzel, N. (2021). Digitale Räume geben und
nehmen: Unterrichtsinteraktion in DSH-Kursen während der COVID-19-Pandemie. Informationen
Deutsch als Fremdsprache, 48(5), 496–515. https://doi.org/10.1515/infodaf-2021-0069'
bibtex: '@article{Drumm_Müller_Stenzel_2021, title={Digitale Räume geben und nehmen:
Unterrichtsinteraktion in DSH-Kursen während der COVID-19-Pandemie}, volume={48},
DOI={10.1515/infodaf-2021-0069},
number={5}, journal={Informationen Deutsch als Fremdsprache}, author={Drumm, Sandra
and Müller, Mareike and Stenzel, Nadja}, year={2021}, pages={496–515} }'
chicago: 'Drumm, Sandra, Mareike Müller, and Nadja Stenzel. “Digitale Räume geben
und nehmen: Unterrichtsinteraktion in DSH-Kursen während der COVID-19-Pandemie.”
Informationen Deutsch als Fremdsprache 48, no. 5 (2021): 496–515. https://doi.org/10.1515/infodaf-2021-0069.'
ieee: 'S. Drumm, M. Müller, and N. Stenzel, “Digitale Räume geben und nehmen: Unterrichtsinteraktion
in DSH-Kursen während der COVID-19-Pandemie,” Informationen Deutsch als Fremdsprache,
vol. 48, no. 5, pp. 496–515, 2021, doi: 10.1515/infodaf-2021-0069.'
mla: 'Drumm, Sandra, et al. “Digitale Räume geben und nehmen: Unterrichtsinteraktion
in DSH-Kursen während der COVID-19-Pandemie.” Informationen Deutsch als Fremdsprache,
vol. 48, no. 5, 2021, pp. 496–515, doi:10.1515/infodaf-2021-0069.'
short: S. Drumm, M. Müller, N. Stenzel, Informationen Deutsch als Fremdsprache 48
(2021) 496–515.
date_created: 2021-12-07T10:32:28Z
date_updated: 2022-01-06T06:58:02Z
department:
- _id: '468'
doi: 10.1515/infodaf-2021-0069
intvolume: ' 48'
issue: '5'
keyword:
- German language courses at university
- interaction
- digital space
- language learning/teaching via videoconference
language:
- iso: ger
page: 496-515
publication: Informationen Deutsch als Fremdsprache
publication_identifier:
issn:
- 2511-0853
- 0724-9616
publication_status: published
status: public
title: 'Digitale Räume geben und nehmen: Unterrichtsinteraktion in DSH-Kursen während
der COVID-19-Pandemie'
type: journal_article
user_id: '71540'
volume: 48
year: '2021'
...
---
_id: '26049'
abstract:
- lang: eng
text: 'Content is the new oil. Users consume billions of terabytes a day while surfing
on news sites or blogs, posting on social media sites, and sending chat messages
around the globe. While content is heterogeneous, the dominant form of web content
is text. There are situations where more diversity needs to be introduced into
text content, for example, to reuse it on websites or to allow a chatbot to base
its models on the information conveyed rather than of the language used. In order
to achieve this, paraphrasing techniques have been developed: One example is Text
spinning, a technique that automatically paraphrases text while leaving the intent
intact. This makes it easier to reuse content, or to change the language generated
by the bot more human. One method for modifying texts is a combination of translation
and back-translation. This paper presents NATTS, a naive approach that uses transformer-based
translation models to create diversified text, combining translation steps in
one model. An advantage of this approach is that it can be fine-tuned and handle
technical language.'
author:
- first_name: Frederik Simon
full_name: Bäumer, Frederik Simon
last_name: Bäumer
- first_name: Joschka
full_name: Kersting, Joschka
id: '58701'
last_name: Kersting
- first_name: Sergej
full_name: Denisov, Sergej
last_name: Denisov
- first_name: Michaela
full_name: Geierhos, Michaela
id: '42496'
last_name: Geierhos
orcid: 0000-0002-8180-5606
citation:
ama: 'Bäumer FS, Kersting J, Denisov S, Geierhos M. IN OTHER WORDS: A NAIVE APPROACH
TO TEXT SPINNING. In: PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET
2021 AND APPLIED COMPUTING 2021. IADIS; 2021:221--225.'
apa: 'Bäumer, F. S., Kersting, J., Denisov, S., & Geierhos, M. (2021). IN OTHER
WORDS: A NAIVE APPROACH TO TEXT SPINNING. PROCEEDINGS OF THE INTERNATIONAL
CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021, 221--225.'
bibtex: '@inproceedings{Bäumer_Kersting_Denisov_Geierhos_2021, title={IN OTHER WORDS:
A NAIVE APPROACH TO TEXT SPINNING}, booktitle={PROCEEDINGS OF THE INTERNATIONAL
CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021}, publisher={IADIS},
author={Bäumer, Frederik Simon and Kersting, Joschka and Denisov, Sergej and Geierhos,
Michaela}, year={2021}, pages={221--225} }'
chicago: 'Bäumer, Frederik Simon, Joschka Kersting, Sergej Denisov, and Michaela
Geierhos. “IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING.” In PROCEEDINGS
OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021,
221--225. IADIS, 2021.'
ieee: 'F. S. Bäumer, J. Kersting, S. Denisov, and M. Geierhos, “IN OTHER WORDS:
A NAIVE APPROACH TO TEXT SPINNING,” in PROCEEDINGS OF THE INTERNATIONAL CONFERENCES
ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021, Lisbon, Portugal, 2021, pp.
221--225.'
mla: 'Bäumer, Frederik Simon, et al. “IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING.”
PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED
COMPUTING 2021, IADIS, 2021, pp. 221--225.'
short: 'F.S. Bäumer, J. Kersting, S. Denisov, M. Geierhos, in: PROCEEDINGS OF THE
INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND APPLIED COMPUTING 2021, IADIS,
2021, pp. 221--225.'
conference:
end_date: 15.10.2021
location: Lisbon, Portugal
name: 18th International Conference on Applied Computing
start_date: 13.10.2021
date_created: 2021-10-11T15:26:58Z
date_updated: 2022-01-06T06:57:16Z
ddc:
- '000'
file:
- access_level: closed
content_type: application/pdf
creator: jkers
date_created: 2021-10-15T15:54:41Z
date_updated: 2021-10-15T15:54:41Z
file_id: '26282'
file_name: Bäumer et al. (2021), Baeumer2021.pdf
file_size: 411667
relation: main_file
success: 1
file_date_updated: 2021-10-15T15:54:41Z
has_accepted_license: '1'
keyword:
- Software Requirements
- Natural Language Processing
- Transfer Learning
- On-The-Fly Computing
language:
- iso: eng
page: 221--225
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
publication: PROCEEDINGS OF THE INTERNATIONAL CONFERENCES ON WWW/INTERNET 2021 AND
APPLIED COMPUTING 2021
publisher: IADIS
status: public
title: 'IN OTHER WORDS: A NAIVE APPROACH TO TEXT SPINNING'
type: conference
user_id: '58701'
year: '2021'
...
---
_id: '21004'
abstract:
- lang: eng
text: 'Automated machine learning (AutoML) supports the algorithmic construction
and data-specific customization of machine learning pipelines, including the selection,
combination, and parametrization of machine learning algorithms as main constituents.
Generally speaking, AutoML approaches comprise two major components: a search
space model and an optimizer for traversing the space. Recent approaches have
shown impressive results in the realm of supervised learning, most notably (single-label)
classification (SLC). Moreover, first attempts at extending these approaches towards
multi-label classification (MLC) have been made. While the space of candidate
pipelines is already huge in SLC, the complexity of the search space is raised
to an even higher power in MLC. One may wonder, therefore, whether and to what
extent optimizers established for SLC can scale to this increased complexity,
and how they compare to each other. This paper makes the following contributions:
First, we survey existing approaches to AutoML for MLC. Second, we augment these
approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking
framework that supports a fair and systematic comparison. Fourth, we conduct an
extensive experimental study, evaluating the methods on a suite of MLC problems.
We find a grammar-based best-first search to compare favorably to other optimizers.'
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification:
Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and
Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276'
apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML
for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions
on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276'
bibtex: '@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label
Classification: Overview and Empirical Evaluation}, DOI={10.1109/tpami.2021.3051276},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever,
Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke},
year={2021}, pages={1–1} }'
chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier.
“AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE
Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1. https://doi.org/10.1109/tpami.2021.3051276.'
ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label
Classification: Overview and Empirical Evaluation,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051276.'
mla: 'Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview
and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.'
short: M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern
Analysis and Machine Intelligence (2021) 1–1.
date_created: 2021-01-16T14:48:13Z
date_updated: 2022-01-06T06:54:42Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
doi: 10.1109/tpami.2021.3051276
keyword:
- Automated Machine Learning
- Multi Label Classification
- Hierarchical Planning
- Bayesian Optimization
language:
- iso: eng
page: 1-1
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
issn:
- 0162-8828
- 2160-9292
- 1939-3539
publication_status: published
status: public
title: 'AutoML for Multi-Label Classification: Overview and Empirical Evaluation'
type: journal_article
user_id: '5786'
year: '2021'
...
---
_id: '21005'
abstract:
- lang: eng
text: Data-parallel applications are developed using different data programming
models, e.g., MapReduce, partition/aggregate. These models represent diverse resource
requirements of application in a datacenter network, which can be represented
by the coflow abstraction. The conventional method of creating hand-crafted coflow
heuristics for admission or scheduling for different workloads is practically
infeasible. In this paper, we propose a deep reinforcement learning (DRL)-based
coflow admission scheme -- LCS -- that can learn an admission policy for a higher-level
performance objective, i.e., maximize successful coflow admissions, without manual
feature engineering. LCS is trained on a production trace, which has online coflow
arrivals. The evaluation results show that LCS is able to learn a reasonable admission
policy that admits more coflows than state-of-the-art Varys heuristic while meeting
their deadlines.
author:
- first_name: Asif
full_name: Hasnain, Asif
id: '63288'
last_name: Hasnain
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Hasnain A, Karl H. Learning Coflow Admissions. In: IEEE INFOCOM 2021 -
IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE
Communications Society. doi:10.1109/INFOCOMWKSHPS51825.2021.9484599'
apa: 'Hasnain, A., & Karl, H. (n.d.). Learning Coflow Admissions. In IEEE
INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).
Vancouver BC Canada: IEEE Communications Society. https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599'
bibtex: '@inproceedings{Hasnain_Karl, title={Learning Coflow Admissions}, DOI={10.1109/INFOCOMWKSHPS51825.2021.9484599},
booktitle={IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops
(INFOCOM WKSHPS)}, publisher={IEEE Communications Society}, author={Hasnain, Asif
and Karl, Holger} }'
chicago: Hasnain, Asif, and Holger Karl. “Learning Coflow Admissions.” In IEEE
INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).
IEEE Communications Society, n.d. https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484599.
ieee: A. Hasnain and H. Karl, “Learning Coflow Admissions,” in IEEE INFOCOM 2021
- IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Vancouver
BC Canada.
mla: Hasnain, Asif, and Holger Karl. “Learning Coflow Admissions.” IEEE INFOCOM
2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS),
IEEE Communications Society, doi:10.1109/INFOCOMWKSHPS51825.2021.9484599.
short: 'A. Hasnain, H. Karl, in: IEEE INFOCOM 2021 - IEEE Conference on Computer
Communications Workshops (INFOCOM WKSHPS), IEEE Communications Society, n.d.'
conference:
end_date: 2021-05-13
location: Vancouver BC Canada
name: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications
start_date: 2021-05-10
date_created: 2021-01-16T18:24:19Z
date_updated: 2022-01-06T06:54:42Z
ddc:
- '000'
department:
- _id: '75'
doi: 10.1109/INFOCOMWKSHPS51825.2021.9484599
keyword:
- Coflow scheduling
- Reinforcement learning
- Deadlines
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9484599
project:
- _id: '16'
name: SFB 901 - Subproject C4
- _id: '4'
name: SFB 901 - Project Area C
- _id: '1'
name: SFB 901
publication: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops
(INFOCOM WKSHPS)
publication_status: accepted
publisher: IEEE Communications Society
related_material:
link:
- relation: confirmation
url: https://ieeexplore.ieee.org/document/9484599
status: public
title: Learning Coflow Admissions
type: conference
user_id: '63288'
year: '2021'
...
---
_id: '21479'
abstract:
- lang: eng
text: Two of the most important metrics when developing Wireless Sensor Networks
(WSNs) applications are the Quality of Information (QoI) and Quality of Service
(QoS). The former is used to specify the quality of the collected data by the
sensors (e.g., measurements error or signal's intensity), while the latter defines
the network's performance and availability (e.g., packet losses and latency).
In this paper, we consider an example of wireless acoustic sensor networks, where
we select a subset of microphones for two different objectives. First, we maximize
the recording quality under QoS constraints. Second, we apply a trade-off between
QoI and QoS. We formulate the problem as a constrained Markov Decision Problem
(MDP) and solve it using reinforcement learning (RL). We compare the RL solution
to a baseline model and show that in case of QoS-guarantee objective, the RL solution
has an optimality gap up to 1\%. Meanwhile, the RL solution is better than the
baseline with improvements up to 23\%, when using the trade-off objective.
author:
- first_name: Haitham
full_name: Afifi, Haitham
id: '65718'
last_name: Afifi
- first_name: Arunselvan
full_name: Ramaswamy, Arunselvan
id: '66937'
last_name: Ramaswamy
orcid: https://orcid.org/ 0000-0001-7547-8111
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Afifi H, Ramaswamy A, Karl H. A Reinforcement Learning QoI/QoS-Aware Approach
in Acoustic Sensor Networks. In: 2021 IEEE 18th Annual Consumer Communications
\& Networking Conference (CCNC) (CCNC 2021). ; 2021.'
apa: Afifi, H., Ramaswamy, A., & Karl, H. (2021). A Reinforcement Learning QoI/QoS-Aware
Approach in Acoustic Sensor Networks. In 2021 IEEE 18th Annual Consumer Communications
\& Networking Conference (CCNC) (CCNC 2021).
bibtex: '@inproceedings{Afifi_Ramaswamy_Karl_2021, title={A Reinforcement Learning
QoI/QoS-Aware Approach in Acoustic Sensor Networks}, booktitle={2021 IEEE 18th
Annual Consumer Communications \& Networking Conference (CCNC) (CCNC 2021)},
author={Afifi, Haitham and Ramaswamy, Arunselvan and Karl, Holger}, year={2021}
}'
chicago: Afifi, Haitham, Arunselvan Ramaswamy, and Holger Karl. “A Reinforcement
Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks.” In 2021 IEEE
18th Annual Consumer Communications \& Networking Conference (CCNC) (CCNC
2021), 2021.
ieee: H. Afifi, A. Ramaswamy, and H. Karl, “A Reinforcement Learning QoI/QoS-Aware
Approach in Acoustic Sensor Networks,” in 2021 IEEE 18th Annual Consumer Communications
\& Networking Conference (CCNC) (CCNC 2021), 2021.
mla: Afifi, Haitham, et al. “A Reinforcement Learning QoI/QoS-Aware Approach in
Acoustic Sensor Networks.” 2021 IEEE 18th Annual Consumer Communications \&
Networking Conference (CCNC) (CCNC 2021), 2021.
short: 'H. Afifi, A. Ramaswamy, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications
\& Networking Conference (CCNC) (CCNC 2021), 2021.'
date_created: 2021-03-12T16:03:53Z
date_updated: 2022-01-06T06:55:00Z
keyword:
- reinforcement learning
- wireless sensor networks
- resource allocation
- acoustic sensor networks
language:
- iso: eng
project:
- _id: '27'
name: Akustische Sensornetzwerke - Teilprojekt "Verteilte akustische Signalverarbeitung
über funkbasierte Sensornetzwerke
publication: 2021 IEEE 18th Annual Consumer Communications \& Networking Conference
(CCNC) (CCNC 2021)
status: public
title: A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks
type: conference
user_id: '65718'
year: '2021'
...
---
_id: '21543'
abstract:
- lang: eng
text: "Services often consist of multiple chained components such as microservices
in a service mesh, or machine learning functions in a pipeline. Providing these
services requires online coordination including scaling the service, placing instance
of all components in the network, scheduling traffic to these instances, and routing
traffic through the network. Optimized service coordination is still a hard problem
due to many influencing factors such as rapidly arriving user demands and limited
node and link capacity. Existing approaches to solve the problem are often built
on rigid models and assumptions, tailored to specific scenarios. If the scenario
changes and the assumptions no longer hold, they easily break and require manual
adjustments by experts. Novel self-learning approaches using deep reinforcement
learning (DRL) are promising but still have limitations as they only address simplified
versions of the problem and are typically centralized and thus do not scale to
practical large-scale networks.\r\n\r\nTo address these issues, we propose a distributed
self-learning service coordination approach using DRL. After centralized training,
we deploy a distributed DRL agent at each node in the network, making fast coordination
decisions locally in parallel with the other nodes. Each agent only observes its
direct neighbors and does not need global knowledge. Hence, our approach scales
independently from the size of the network. In our extensive evaluation using
real-world network topologies and traffic traces, we show that our proposed approach
outperforms a state-of-the-art conventional heuristic as well as a centralized
DRL approach (60% higher throughput on average) while requiring less time per
online decision (1 ms)."
author:
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Haydar
full_name: Qarawlus, Haydar
last_name: Qarawlus
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Schneider SB, Qarawlus H, Karl H. Distributed Online Service Coordination
Using Deep Reinforcement Learning. In: IEEE International Conference on Distributed
Computing Systems (ICDCS). IEEE; 2021.'
apa: 'Schneider, S. B., Qarawlus, H., & Karl, H. (2021). Distributed Online
Service Coordination Using Deep Reinforcement Learning. In IEEE International
Conference on Distributed Computing Systems (ICDCS). Washington, DC, USA:
IEEE.'
bibtex: '@inproceedings{Schneider_Qarawlus_Karl_2021, title={Distributed Online
Service Coordination Using Deep Reinforcement Learning}, booktitle={IEEE International
Conference on Distributed Computing Systems (ICDCS)}, publisher={IEEE}, author={Schneider,
Stefan Balthasar and Qarawlus, Haydar and Karl, Holger}, year={2021} }'
chicago: Schneider, Stefan Balthasar, Haydar Qarawlus, and Holger Karl. “Distributed
Online Service Coordination Using Deep Reinforcement Learning.” In IEEE International
Conference on Distributed Computing Systems (ICDCS). IEEE, 2021.
ieee: S. B. Schneider, H. Qarawlus, and H. Karl, “Distributed Online Service Coordination
Using Deep Reinforcement Learning,” in IEEE International Conference on Distributed
Computing Systems (ICDCS), Washington, DC, USA, 2021.
mla: Schneider, Stefan Balthasar, et al. “Distributed Online Service Coordination
Using Deep Reinforcement Learning.” IEEE International Conference on Distributed
Computing Systems (ICDCS), IEEE, 2021.
short: 'S.B. Schneider, H. Qarawlus, H. Karl, in: IEEE International Conference
on Distributed Computing Systems (ICDCS), IEEE, 2021.'
conference:
location: Washington, DC, USA
name: IEEE International Conference on Distributed Computing Systems (ICDCS)
date_created: 2021-03-18T17:15:47Z
date_updated: 2022-01-06T06:55:04Z
ddc:
- '000'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2021-03-18T17:12:56Z
date_updated: 2021-03-18T17:12:56Z
file_id: '21544'
file_name: public_author_version.pdf
file_size: 606321
relation: main_file
title: Distributed Online Service Coordination Using Deep Reinforcement Learning
file_date_updated: 2021-03-18T17:12:56Z
has_accepted_license: '1'
keyword:
- network management
- service management
- coordination
- reinforcement learning
- distributed
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
publication: IEEE International Conference on Distributed Computing Systems (ICDCS)
publisher: IEEE
related_material:
link:
- relation: software
url: https://github.com/ RealVNF/distributed-drl-coordination
status: public
title: Distributed Online Service Coordination Using Deep Reinforcement Learning
type: conference
user_id: '35343'
year: '2021'
...
---
_id: '22481'
abstract:
- lang: eng
text: During the industrial processing of materials for the manufacture of new products,
surface defects can quickly occur. In order to achieve high quality without a
long time delay, it makes sense to inspect the work pieces so that defective work
pieces can be sorted out right at the beginning of the process. At the same time,
the evaluation unit should come close the perception of the human eye regarding
detection of defects in surfaces. Such defects often manifest themselves by a
deviation of the existing structure. The only restriction should be that only
matt surfaces should be considered here. Therefore in this work, different classification
and image processing algorithms are applied to surface data to identify possible
surface damages. For this purpose, the Gabor filter and the FST (Fused Structure
and Texture) features generated with it, as well as the salience metric are used
on the image processing side. On the classification side, however, deep neural
networks, Convolutional Neural Networks (CNN), and autoencoders are used to make
a decision. A distinction is also made between training using class labels and
without. It turns out later that the salience metric are best performed by CNN.
On the other hand, if there is no labeled training data available, a novelty classification
can easily be achieved by using autoencoders as well as the salience metric and
some filters.
author:
- first_name: Tom
full_name: Sander, Tom
last_name: Sander
- first_name: Sven
full_name: Lange, Sven
id: '38240'
last_name: Lange
- first_name: Ulrich
full_name: Hilleringmann, Ulrich
last_name: Hilleringmann
- first_name: Volker
full_name: Geneis, Volker
last_name: Geneis
- first_name: Christian
full_name: Hedayat, Christian
last_name: Hedayat
- first_name: Harald
full_name: Kuhn, Harald
last_name: Kuhn
- first_name: Franz-Barthold
full_name: Gockel, Franz-Barthold
last_name: Gockel
citation:
ama: 'Sander T, Lange S, Hilleringmann U, et al. Detection of Defects on Irregular
Structured Surfaces by Image Processing Methods for Feature Extraction. In: 22nd
IEEE International Conference on Industrial Technology (ICIT). Valencia, Spain
: IEEE; 2021. doi:10.1109/icit46573.2021.9453646'
apa: 'Sander, T., Lange, S., Hilleringmann, U., Geneis, V., Hedayat, C., Kuhn, H.,
& Gockel, F.-B. (2021). Detection of Defects on Irregular Structured Surfaces
by Image Processing Methods for Feature Extraction. In 22nd IEEE International
Conference on Industrial Technology (ICIT). Valencia, Spain : IEEE. https://doi.org/10.1109/icit46573.2021.9453646'
bibtex: '@inproceedings{Sander_Lange_Hilleringmann_Geneis_Hedayat_Kuhn_Gockel_2021,
place={Valencia, Spain }, title={Detection of Defects on Irregular Structured
Surfaces by Image Processing Methods for Feature Extraction}, DOI={10.1109/icit46573.2021.9453646},
booktitle={22nd IEEE International Conference on Industrial Technology (ICIT)},
publisher={IEEE}, author={Sander, Tom and Lange, Sven and Hilleringmann, Ulrich
and Geneis, Volker and Hedayat, Christian and Kuhn, Harald and Gockel, Franz-Barthold},
year={2021} }'
chicago: 'Sander, Tom, Sven Lange, Ulrich Hilleringmann, Volker Geneis, Christian
Hedayat, Harald Kuhn, and Franz-Barthold Gockel. “Detection of Defects on Irregular
Structured Surfaces by Image Processing Methods for Feature Extraction.” In 22nd
IEEE International Conference on Industrial Technology (ICIT). Valencia, Spain
: IEEE, 2021. https://doi.org/10.1109/icit46573.2021.9453646.'
ieee: T. Sander et al., “Detection of Defects on Irregular Structured Surfaces
by Image Processing Methods for Feature Extraction,” in 22nd IEEE International
Conference on Industrial Technology (ICIT), Valencia, Spain , 2021.
mla: Sander, Tom, et al. “Detection of Defects on Irregular Structured Surfaces
by Image Processing Methods for Feature Extraction.” 22nd IEEE International
Conference on Industrial Technology (ICIT), IEEE, 2021, doi:10.1109/icit46573.2021.9453646.
short: 'T. Sander, S. Lange, U. Hilleringmann, V. Geneis, C. Hedayat, H. Kuhn, F.-B.
Gockel, in: 22nd IEEE International Conference on Industrial Technology (ICIT),
IEEE, Valencia, Spain , 2021.'
conference:
end_date: 2021-03-12
location: 'Valencia, Spain '
name: 22nd IEEE International Conference on Industrial Technology (ICIT)
start_date: 2021-03-10
date_created: 2021-06-20T23:32:11Z
date_updated: 2022-01-06T06:55:33Z
department:
- _id: '59'
- _id: '485'
doi: 10.1109/icit46573.2021.9453646
keyword:
- Image Processing
- Defect Detection
- wooden surfaces
- Machine Learning
- Neural Networks
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9453646
place: 'Valencia, Spain '
publication: 22nd IEEE International Conference on Industrial Technology (ICIT)
publication_identifier:
isbn:
- '9781728157306'
publication_status: published
publisher: IEEE
status: public
title: Detection of Defects on Irregular Structured Surfaces by Image Processing Methods
for Feature Extraction
type: conference
user_id: '38240'
year: '2021'
...
---
_id: '21808'
abstract:
- lang: eng
text: "Modern services consist of interconnected components,e.g., microservices
in a service mesh or machine learning functions in a pipeline. These services
can scale and run across multiple network nodes on demand. To process incoming
traffic, service components have to be instantiated and traffic assigned to these
instances, taking capacities, changing demands, and Quality of Service (QoS) requirements
into account. This challenge is usually solved with custom approaches designed
by experts. While this typically works well for the considered scenario, the models
often rely on unrealistic assumptions or on knowledge that is not available in
practice (e.g., a priori knowledge).\r\n\r\nWe propose DeepCoord, a novel deep
reinforcement learning approach that learns how to best coordinate services and
is geared towards realistic assumptions. It interacts with the network and relies
on available, possibly delayed monitoring information. Rather than defining a
complex model or an algorithm on how to achieve an objective, our model-free approach
adapts to various objectives and traffic patterns. An agent is trained offline
without expert knowledge and then applied online with minimal overhead. Compared
to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput
(up to 76%) and overall network utility (more than 2x) on realworld network topologies
and traffic traces. It also supports optimizing multiple, possibly competing objectives,
learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic
traffic, and scales to large real-world networks. For reproducibility and reuse,
our code is publicly available."
article_type: original
author:
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Ramin
full_name: Khalili, Ramin
last_name: Khalili
- first_name: Adnan
full_name: Manzoor, Adnan
last_name: Manzoor
- first_name: Haydar
full_name: Qarawlus, Haydar
last_name: Qarawlus
- first_name: Rafael
full_name: Schellenberg, Rafael
last_name: Schellenberg
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
- first_name: Artur
full_name: Hecker, Artur
last_name: Hecker
citation:
ama: Schneider SB, Khalili R, Manzoor A, et al. Self-Learning Multi-Objective Service
Coordination Using Deep Reinforcement Learning. Transactions on Network and
Service Management. 2021. doi:10.1109/TNSM.2021.3076503
apa: Schneider, S. B., Khalili, R., Manzoor, A., Qarawlus, H., Schellenberg, R.,
Karl, H., & Hecker, A. (2021). Self-Learning Multi-Objective Service Coordination
Using Deep Reinforcement Learning. Transactions on Network and Service Management.
https://doi.org/10.1109/TNSM.2021.3076503
bibtex: '@article{Schneider_Khalili_Manzoor_Qarawlus_Schellenberg_Karl_Hecker_2021,
title={Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement
Learning}, DOI={10.1109/TNSM.2021.3076503},
journal={Transactions on Network and Service Management}, publisher={IEEE}, author={Schneider,
Stefan Balthasar and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and
Schellenberg, Rafael and Karl, Holger and Hecker, Artur}, year={2021} }'
chicago: Schneider, Stefan Balthasar, Ramin Khalili, Adnan Manzoor, Haydar Qarawlus,
Rafael Schellenberg, Holger Karl, and Artur Hecker. “Self-Learning Multi-Objective
Service Coordination Using Deep Reinforcement Learning.” Transactions on Network
and Service Management, 2021. https://doi.org/10.1109/TNSM.2021.3076503.
ieee: S. B. Schneider et al., “Self-Learning Multi-Objective Service Coordination
Using Deep Reinforcement Learning,” Transactions on Network and Service Management,
2021.
mla: Schneider, Stefan Balthasar, et al. “Self-Learning Multi-Objective Service
Coordination Using Deep Reinforcement Learning.” Transactions on Network and
Service Management, IEEE, 2021, doi:10.1109/TNSM.2021.3076503.
short: S.B. Schneider, R. Khalili, A. Manzoor, H. Qarawlus, R. Schellenberg, H.
Karl, A. Hecker, Transactions on Network and Service Management (2021).
date_created: 2021-04-27T08:04:16Z
date_updated: 2022-01-06T06:55:15Z
ddc:
- '000'
department:
- _id: '75'
doi: 10.1109/TNSM.2021.3076503
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2021-04-27T08:01:26Z
date_updated: 2021-04-27T08:01:26Z
description: Author version of the accepted paper
file_id: '21809'
file_name: ris-accepted-version.pdf
file_size: 4172270
relation: main_file
file_date_updated: 2021-04-27T08:01:26Z
has_accepted_license: '1'
keyword:
- network management
- service management
- coordination
- reinforcement learning
- self-learning
- self-adaptation
- multi-objective
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '4'
name: SFB 901 - Project Area C
- _id: '16'
name: SFB 901 - Subproject C4
publication: Transactions on Network and Service Management
publisher: IEEE
status: public
title: Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement
Learning
type: journal_article
user_id: '35343'
year: '2021'
...
---
_id: '27381'
abstract:
- lang: eng
text: Graph neural networks (GNNs) have been successfully applied in many structured
data domains, with applications ranging from molecular property prediction to
the analysis of social networks. Motivated by the broad applicability of GNNs,
we propose the family of so-called RankGNNs, a combination of neural Learning
to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences
between graphs, suggesting that one of them is preferred over the other. One practical
application of this problem is drug screening, where an expert wants to find the
most promising molecules in a large collection of drug candidates. We empirically
demonstrate that our proposed pair-wise RankGNN approach either significantly
outperforms or at least matches the ranking performance of the naive point-wise
baseline approach, in which the LtR problem is solved via GNN-based graph regression.
author:
- first_name: Clemens
full_name: Damke, Clemens
id: '48192'
last_name: Damke
orcid: 0000-0002-0455-0048
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Damke C, Hüllermeier E. Ranking Structured Objects with Graph Neural Networks.
In: Soares C, Torgo L, eds. Proceedings of The 24th International Conference
on Discovery Science (DS 2021). Vol 12986. Lecture Notes in Computer Science.
Springer; 2021:166-180. doi:10.1007/978-3-030-88942-5'
apa: Damke, C., & Hüllermeier, E. (2021). Ranking Structured Objects with Graph
Neural Networks. In C. Soares & L. Torgo (Eds.), Proceedings of The 24th
International Conference on Discovery Science (DS 2021) (Vol. 12986, pp. 166–180).
Springer. https://doi.org/10.1007/978-3-030-88942-5
bibtex: '@inproceedings{Damke_Hüllermeier_2021, series={Lecture Notes in Computer
Science}, title={Ranking Structured Objects with Graph Neural Networks}, volume={12986},
DOI={10.1007/978-3-030-88942-5},
booktitle={Proceedings of The 24th International Conference on Discovery Science
(DS 2021)}, publisher={Springer}, author={Damke, Clemens and Hüllermeier, Eyke},
editor={Soares, Carlos and Torgo, Luis}, year={2021}, pages={166–180}, collection={Lecture
Notes in Computer Science} }'
chicago: Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with
Graph Neural Networks.” In Proceedings of The 24th International Conference
on Discovery Science (DS 2021), edited by Carlos Soares and Luis Torgo, 12986:166–80.
Lecture Notes in Computer Science. Springer, 2021. https://doi.org/10.1007/978-3-030-88942-5.
ieee: 'C. Damke and E. Hüllermeier, “Ranking Structured Objects with Graph Neural
Networks,” in Proceedings of The 24th International Conference on Discovery
Science (DS 2021), Halifax, Canada, 2021, vol. 12986, pp. 166–180, doi: 10.1007/978-3-030-88942-5.'
mla: Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph
Neural Networks.” Proceedings of The 24th International Conference on Discovery
Science (DS 2021), edited by Carlos Soares and Luis Torgo, vol. 12986, Springer,
2021, pp. 166–80, doi:10.1007/978-3-030-88942-5.
short: 'C. Damke, E. Hüllermeier, in: C. Soares, L. Torgo (Eds.), Proceedings of
The 24th International Conference on Discovery Science (DS 2021), Springer, 2021,
pp. 166–180.'
conference:
end_date: 2021-10-13
location: Halifax, Canada
name: 24th International Conference on Discovery Science
start_date: 2021-10-11
date_created: 2021-11-11T14:15:18Z
date_updated: 2022-04-11T22:08:12Z
department:
- _id: '355'
doi: 10.1007/978-3-030-88942-5
editor:
- first_name: Carlos
full_name: Soares, Carlos
last_name: Soares
- first_name: Luis
full_name: Torgo, Luis
last_name: Torgo
external_id:
arxiv:
- '2104.08869'
intvolume: ' 12986'
keyword:
- Graph-structured data
- Graph neural networks
- Preference learning
- Learning to rank
language:
- iso: eng
page: 166-180
publication: Proceedings of The 24th International Conference on Discovery Science
(DS 2021)
publication_identifier:
isbn:
- '9783030889418'
- '9783030889425'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer
quality_controlled: '1'
series_title: Lecture Notes in Computer Science
status: public
title: Ranking Structured Objects with Graph Neural Networks
type: conference
user_id: '48192'
volume: 12986
year: '2021'
...
---
_id: '20212'
abstract:
- lang: eng
text: "Ideational impact refers to the uptake of a paper's ideas and concepts by
subsequent research. It is defined in stark contrast to total citation impact,
a measure predominantly used in research evaluation that assumes that all citations
are equal. Understanding ideational impact is critical for evaluating research
impact and understanding how scientific disciplines build a cumulative tradition.
Research has only recently developed automated citation classification techniques
to distinguish between different types of citations and generally does not emphasize
the conceptual content of the citations and its ideational impact. To address
this problem, we develop Deep Content-enriched Ideational Impact Classification
(Deep-CENIC) as the first automated approach for ideational impact classification
to support researchers' literature search practices. We evaluate Deep-CENIC on
1,256 papers citing 24 information systems review articles from the IT business
value domain. We show that Deep-CENIC significantly outperforms state-of-the-art
benchmark models. We contribute to information systems research by operationalizing
the concept of ideational impact, designing a recommender system for academic
papers based on deep learning techniques, and empirically exploring the ideational
impact of the IT business value domain.\r\n"
article_number: '113432'
author:
- first_name: Julian
full_name: Prester, Julian
last_name: Prester
- first_name: Gerit
full_name: Wagner, Gerit
last_name: Wagner
- first_name: Guido
full_name: Schryen, Guido
id: '72850'
last_name: Schryen
- first_name: Nik Rushdi
full_name: Hassan, Nik Rushdi
last_name: Hassan
citation:
ama: 'Prester J, Wagner G, Schryen G, Hassan NR. Classifying the Ideational Impact
of Information Systems Review Articles: A Content-Enriched Deep Learning Approach.
Decision Support Systems. 2021;140(January).'
apa: 'Prester, J., Wagner, G., Schryen, G., & Hassan, N. R. (2021). Classifying
the Ideational Impact of Information Systems Review Articles: A Content-Enriched
Deep Learning Approach. Decision Support Systems, 140(January),
Article 113432.'
bibtex: '@article{Prester_Wagner_Schryen_Hassan_2021, title={Classifying the Ideational
Impact of Information Systems Review Articles: A Content-Enriched Deep Learning
Approach}, volume={140}, number={January113432}, journal={Decision Support Systems},
author={Prester, Julian and Wagner, Gerit and Schryen, Guido and Hassan, Nik Rushdi},
year={2021} }'
chicago: 'Prester, Julian, Gerit Wagner, Guido Schryen, and Nik Rushdi Hassan. “Classifying
the Ideational Impact of Information Systems Review Articles: A Content-Enriched
Deep Learning Approach.” Decision Support Systems 140, no. January (2021).'
ieee: 'J. Prester, G. Wagner, G. Schryen, and N. R. Hassan, “Classifying the Ideational
Impact of Information Systems Review Articles: A Content-Enriched Deep Learning
Approach,” Decision Support Systems, vol. 140, no. January, Art. no. 113432,
2021.'
mla: 'Prester, Julian, et al. “Classifying the Ideational Impact of Information
Systems Review Articles: A Content-Enriched Deep Learning Approach.” Decision
Support Systems, vol. 140, no. January, 113432, 2021.'
short: J. Prester, G. Wagner, G. Schryen, N.R. Hassan, Decision Support Systems
140 (2021).
date_created: 2020-10-27T13:28:21Z
date_updated: 2022-06-10T06:55:32Z
ddc:
- '000'
department:
- _id: '277'
file:
- access_level: open_access
content_type: application/pdf
creator: hsiemes
date_created: 2020-10-27T13:31:01Z
date_updated: 2020-10-27T13:31:01Z
file_id: '20213'
file_name: DECSUP-D-20-00312 - PREPUBLICATION.pdf
file_size: 440903
relation: main_file
file_date_updated: 2020-10-27T13:31:01Z
has_accepted_license: '1'
intvolume: ' 140'
issue: January
keyword:
- Ideational impact
- citation classification
- academic recommender systems
- natural language processing
- deep learning
- cumulative tradition
language:
- iso: eng
oa: '1'
publication: Decision Support Systems
status: public
title: 'Classifying the Ideational Impact of Information Systems Review Articles:
A Content-Enriched Deep Learning Approach'
type: journal_article
user_id: '72850'
volume: 140
year: '2021'
...
---
_id: '33854'
abstract:
- lang: eng
text: "Macrodiversity is a key technique to increase the capacity of mobile networks.
It can be realized using coordinated multipoint (CoMP), simultaneously connecting
users to multiple overlapping cells. Selecting which users to serve by how many
and which cells is NP-hard but needs to happen continuously in real time as users
move and channel state changes. Existing approaches often require strict assumptions
about or perfect knowledge of the underlying radio system, its resource allocation
scheme, or user movements, none of which is readily available in practice.\r\n\r\nInstead,
we propose three novel self-learning and self-adapting approaches using model-free
deep reinforcement learning (DRL): DeepCoMP, DD-CoMP, and D3-CoMP. DeepCoMP leverages
central observations and control of all users to select cells almost optimally.
DD-CoMP and D3-CoMP use multi-agent DRL, which allows distributed, robust, and
highly scalable coordination. All three approaches learn from experience and self-adapt
to varying scenarios, reaching 2x higher Quality of Experience than other approaches.
They have very few built-in assumptions and do not need prior system knowledge,
making them more robust to change and better applicable in practice than existing
approaches."
author:
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
- first_name: Ramin
full_name: Khalili, Ramin
last_name: Khalili
- first_name: Artur
full_name: Hecker, Artur
last_name: Hecker
citation:
ama: 'Schneider SB, Karl H, Khalili R, Hecker A. DeepCoMP: Coordinated Multipoint
Using Multi-Agent Deep Reinforcement Learning.; 2021.'
apa: 'Schneider, S. B., Karl, H., Khalili, R., & Hecker, A. (2021). DeepCoMP:
Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning.'
bibtex: '@book{Schneider_Karl_Khalili_Hecker_2021, title={DeepCoMP: Coordinated
Multipoint Using Multi-Agent Deep Reinforcement Learning}, author={Schneider,
Stefan Balthasar and Karl, Holger and Khalili, Ramin and Hecker, Artur}, year={2021}
}'
chicago: 'Schneider, Stefan Balthasar, Holger Karl, Ramin Khalili, and Artur Hecker.
DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning,
2021.'
ieee: 'S. B. Schneider, H. Karl, R. Khalili, and A. Hecker, DeepCoMP: Coordinated
Multipoint Using Multi-Agent Deep Reinforcement Learning. 2021.'
mla: 'Schneider, Stefan Balthasar, et al. DeepCoMP: Coordinated Multipoint Using
Multi-Agent Deep Reinforcement Learning. 2021.'
short: 'S.B. Schneider, H. Karl, R. Khalili, A. Hecker, DeepCoMP: Coordinated Multipoint
Using Multi-Agent Deep Reinforcement Learning, 2021.'
date_created: 2022-10-20T16:44:19Z
date_updated: 2022-11-18T09:59:27Z
ddc:
- '004'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2022-10-20T16:41:10Z
date_updated: 2022-10-20T16:41:10Z
file_id: '33855'
file_name: preprint.pdf
file_size: 2521656
relation: main_file
file_date_updated: 2022-10-20T16:41:10Z
has_accepted_license: '1'
keyword:
- mobility management
- coordinated multipoint
- CoMP
- cell selection
- resource management
- reinforcement learning
- multi agent
- MARL
- self-learning
- self-adaptation
- QoE
language:
- iso: eng
oa: '1'
project:
- _id: '4'
name: 'SFB 901 - C: SFB 901 - Project Area C'
- _id: '16'
name: 'SFB 901 - C4: SFB 901 - Subproject C4'
- _id: '1'
name: 'SFB 901: SFB 901'
status: public
title: 'DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning'
type: working_paper
user_id: '477'
year: '2021'
...
---
_id: '29102'
abstract:
- lang: ger
text: 'Das Studium der Wirtschaftspädagogik bereitet Studierende auf das didaktische
Handeln in beruflichen Lehr-Lernkontexten (u. a. berufliche Schulen, Ausbildung
in Betrieben) vor. Theorie-Praxis-Verzahnung ist somit aus zwei Perspektiven zu
modellieren: Einerseits geht es um den Aufbau eines fachwissenschaftlichen Verständnisses,
welches von den Handlungszusammenhängen in einer beruflichen Domäne mit kaufmännisch-verwaltenden
Bezügen ausgeht und weniger auf einer rein fachwissenschaftlichen Bildung beruht.
Die zukünftige Berufspraxis der Schülerinnen und Schüler muss in den Blick genommen
werden. Andererseits geht es um die Professionalisierung als pädagogisches Personal,
welches berufsbezogene Lernprozesse fachdidaktisch gestalten kann. Die zukünftige
Lehrpraxis in beruflichen Lehr-Lernkontexten ist in den Blick zu nehmen. Zielstellung
des Beitrages ist es, diese doppelte Theorie-Praxis-Verzahnung als Konstitutionsmerkmal
der Wirtschaftspädagogik aufzuzeigen (Abschn. 2), um darauf basierend anhand von
Theorien des Lernens am Arbeitsplatz Potenziale und Grenzen des Lernortes Praxis
als Beitrag zur Professionalisierung angehender Wirtschaftspädagog*innen im Studium
herauszuarbeiten (Abschn. 3). Am Beispiel des Konzeptes von Universitätsschulen
soll eine Umsetzungsvariante zur Theorie-Praxis-Verzahnung unter Herausarbeitung
der Potenziale der jeweiligen Lernorte Schule und Universität aufgezeigt werden
(Abschn. 4).'
author:
- first_name: Karl-Heinz
full_name: Gerholz, Karl-Heinz
last_name: Gerholz
- first_name: Michael
full_name: Goller, Michael
id: '30984'
last_name: Goller
orcid: 0000-0002-2820-9178
citation:
ama: 'Gerholz K-H, Goller M. Theorie-Praxis-Verzahnung in der Wirtschaftspädagogik:
Potenziale und Grenzen des Lernortes Praxis. In: Caruso C, Harteis C, Gröschner
A, eds. Edition Fachdidaktiken. Springer; 2021:393-419. doi:10.1007/978-3-658-32568-8_22'
apa: 'Gerholz, K.-H., & Goller, M. (2021). Theorie-Praxis-Verzahnung in der
Wirtschaftspädagogik: Potenziale und Grenzen des Lernortes Praxis. In C. Caruso,
C. Harteis, & A. Gröschner (Eds.), Edition Fachdidaktiken (pp. 393–419).
Springer. https://doi.org/10.1007/978-3-658-32568-8_22'
bibtex: '@inbook{Gerholz_Goller_2021, place={Wiesbaden}, title={Theorie-Praxis-Verzahnung
in der Wirtschaftspädagogik: Potenziale und Grenzen des Lernortes Praxis}, DOI={10.1007/978-3-658-32568-8_22},
booktitle={Edition Fachdidaktiken}, publisher={Springer}, author={Gerholz, Karl-Heinz
and Goller, Michael}, editor={Caruso, Carina and Harteis, Christian and Gröschner,
Alexander}, year={2021}, pages={393–419} }'
chicago: 'Gerholz, Karl-Heinz, and Michael Goller. “Theorie-Praxis-Verzahnung in
der Wirtschaftspädagogik: Potenziale und Grenzen des Lernortes Praxis.” In Edition
Fachdidaktiken, edited by Carina Caruso, Christian Harteis, and Alexander
Gröschner, 393–419. Wiesbaden: Springer, 2021. https://doi.org/10.1007/978-3-658-32568-8_22.'
ieee: 'K.-H. Gerholz and M. Goller, “Theorie-Praxis-Verzahnung in der Wirtschaftspädagogik:
Potenziale und Grenzen des Lernortes Praxis,” in Edition Fachdidaktiken,
C. Caruso, C. Harteis, and A. Gröschner, Eds. Wiesbaden: Springer, 2021, pp. 393–419.'
mla: 'Gerholz, Karl-Heinz, and Michael Goller. “Theorie-Praxis-Verzahnung in der
Wirtschaftspädagogik: Potenziale und Grenzen des Lernortes Praxis.” Edition
Fachdidaktiken, edited by Carina Caruso et al., Springer, 2021, pp. 393–419,
doi:10.1007/978-3-658-32568-8_22.'
short: 'K.-H. Gerholz, M. Goller, in: C. Caruso, C. Harteis, A. Gröschner (Eds.),
Edition Fachdidaktiken, Springer, Wiesbaden, 2021, pp. 393–419.'
date_created: 2021-12-22T17:20:50Z
date_updated: 2022-02-03T13:26:19Z
department:
- _id: '452'
doi: 10.1007/978-3-658-32568-8_22
editor:
- first_name: Carina
full_name: Caruso, Carina
last_name: Caruso
- first_name: Christian
full_name: Harteis, Christian
last_name: Harteis
- first_name: Alexander
full_name: Gröschner, Alexander
last_name: Gröschner
keyword:
- Berufliche Lehrerbildung
- Professional Learning
- Theorie-Praxis-Verzahnung
- Wirtschaftspädagogik
- Universitätsschulen
language:
- iso: ger
page: 393-419
place: Wiesbaden
publication: Edition Fachdidaktiken
publication_identifier:
isbn:
- '9783658325671'
- '9783658325688'
issn:
- 2524-8677
- 2524-8685
publication_status: published
publisher: Springer
related_material:
link:
- relation: confirmation
url: https://link.springer.com/chapter/10.1007%2F978-3-658-32568-8_22
status: public
title: 'Theorie-Praxis-Verzahnung in der Wirtschaftspädagogik: Potenziale und Grenzen
des Lernortes Praxis'
type: book_chapter
user_id: '79910'
year: '2021'
...
---
_id: '35202'
abstract:
- lang: eng
text: "Purpose: This study aims at investigating how digitalisation (in the sense
of industry 4.0) has changed the work of farmers and how they experience the changes
from more traditional work to digitalised agriculture. It also investigates what
knowledge farmers require on digitalised farms and how they acquire it. Dairy
farming was used as domain of investigation since it, unlike other industries,
has strongly been affected by digitalisation throughout the last years.\r\n\r\nMethod:
Exploratory interviews with 10 livestock farmers working on digitalised dairy
farms were analysed using qualitative content analysis. A deductive and inductive
coding strategy was used. \r\n\r\nFindings: Farming work has changed from more
manual tasks towards symbol manipulation and data processing. Farmers must be
able to use computers and other digital devices to retrieve and analyse sensor
data that allow them to monitor and control the processes on their farm. For this
new kind of work, farmers require elaborated mental models that link traditional
farming knowledge with knowledge about digital systems, including a strong understanding
of production processes underlying their farm. Learning is mostly based on instructions
offered by manufacturers of the new technology as well as informal and non-formal
learning modes. Even younger farmers report that digital technology was not sufficiently
covered in their (vocational) degrees. In general, farmers emphasises the positive
effects of digitalisation both on their working as well as private life. \r\n\r\nConclusions:
Farmers should be aware of the opportunities as well as the potential drawbacks
of the digitalisation of work processes in agriculture. Providers of agricultural
education (like vocational schools or training institutes) need to incorporate
the knowledge and skills required to work in digitalised environments (e.g., data
literacy) in their syllabi. Further studies are required to assess how digitalisation
changes farming practices and what knowledge as well as skills linked to these
developments are required in the future."
author:
- first_name: Michael
full_name: Goller, Michael
id: '30984'
last_name: Goller
orcid: 0000-0002-2820-9178
- first_name: Carina
full_name: Caruso, Carina
id: '23123'
last_name: Caruso
- first_name: Christian
full_name: Harteis, Christian
id: '27503'
last_name: Harteis
orcid: https://orcid.org/0000-0002-3570-7626
citation:
ama: 'Goller M, Caruso C, Harteis C. Digitalisation in Agriculture: Knowledge and
Learning Requirements of German Dairy Farmers. International Journal for Research
in Vocational Education and Training. 2021;8(2):208–223. doi:10.13152/IJRVET.8.2.4.'
apa: 'Goller, M., Caruso, C., & Harteis, C. (2021). Digitalisation in Agriculture:
Knowledge and Learning Requirements of German Dairy Farmers. International
Journal for Research in Vocational Education and Training, 8(2), 208–223.
https://doi.org/10.13152/IJRVET.8.2.4.'
bibtex: '@article{Goller_Caruso_Harteis_2021, title={Digitalisation in Agriculture:
Knowledge and Learning Requirements of German Dairy Farmers}, volume={8}, DOI={10.13152/IJRVET.8.2.4.}, number={2},
journal={International Journal for Research in Vocational Education and Training},
author={Goller, Michael and Caruso, Carina and Harteis, Christian}, year={2021},
pages={208–223} }'
chicago: 'Goller, Michael, Carina Caruso, and Christian Harteis. “Digitalisation
in Agriculture: Knowledge and Learning Requirements of German Dairy Farmers.”
International Journal for Research in Vocational Education and Training
8, no. 2 (2021): 208–223. https://doi.org/10.13152/IJRVET.8.2.4.'
ieee: 'M. Goller, C. Caruso, and C. Harteis, “Digitalisation in Agriculture: Knowledge
and Learning Requirements of German Dairy Farmers,” International Journal for
Research in Vocational Education and Training, vol. 8, no. 2, pp. 208–223,
2021, doi: 10.13152/IJRVET.8.2.4.'
mla: 'Goller, Michael, et al. “Digitalisation in Agriculture: Knowledge and Learning
Requirements of German Dairy Farmers.” International Journal for Research in
Vocational Education and Training, vol. 8, no. 2, 2021, pp. 208–223, doi:10.13152/IJRVET.8.2.4.'
short: M. Goller, C. Caruso, C. Harteis, International Journal for Research in Vocational
Education and Training 8 (2021) 208–223.
date_created: 2023-01-04T10:08:59Z
date_updated: 2023-01-06T12:17:27Z
doi: 10.13152/IJRVET.8.2.4.
intvolume: ' 8'
issue: '2'
keyword:
- Work-Based Learning
- Organisational Change
- Digital Competences
- Qualitative Research
- Digitalisation
- Farming
- Dairy
- VET
- Vocational Education and Training
language:
- iso: eng
page: 208–223
publication: International Journal for Research in Vocational Education and Training
publication_identifier:
issn:
- 2197-8646
publication_status: published
status: public
title: 'Digitalisation in Agriculture: Knowledge and Learning Requirements of German
Dairy Farmers'
type: journal_article
user_id: '86519'
volume: 8
year: '2021'
...
---
_id: '35464'
abstract:
- lang: eng
text: 'The digital transformation of organizations in the industrial sector is primarily
driven by the opportunity to increase productivity while simultaneously reducing
costs through integration into a cyber-physical system. One way to fully tap the
potential of a cyber-physical system is the concept of the digital twin, i.e.,
the real-time digital representation of machines and resources involved – including
human resources. The vision of representing humans by digital twins primarily
aims at increasing economic benefits. The digital twin of a human, however, cannot
be designed in a similar way to that of a machine. The human digital twin shall
rather enable humans to act within the cyber-physical system. It therefore offers
humans a power of control and the opportunity to provide feedback. The concept
of the digital twin is still in its infancy and raises many questions in particular
from an educational perspective. The contribution aims at answering the following
questions and refers to the example of team learning: Which and how much data
should and may the digital twin contain in order to support humans in their learning?
To what extent will humans be able to control and design their own learning? How
may skills, experiences, and social interactions of humans be represented in the
digital twin; their growth and further development, respectively? With cyber-physical
systems transcending corporate, national, and legal boundaries, what learning
culture will be the frame of reference for the involved organizations?'
author:
- first_name: ' Angelina'
full_name: Berisha-Gawlowski, Angelina
last_name: Berisha-Gawlowski
- first_name: Carina
full_name: Caruso, Carina
id: '23123'
last_name: Caruso
- first_name: Christian
full_name: Harteis, Christian
id: '27503'
last_name: Harteis
orcid: https://orcid.org/0000-0002-3570-7626
citation:
ama: 'Berisha-Gawlowski Angelina, Caruso C, Harteis C. The Concept of a Digital
Twin and Its Potential for Learning Organizations. In: Ifenthaler D, Hofhues S,
Egloffstein M, Helbig C, eds. Digital Transformation of Learning Organizations
. Springer; 2021:95–114. doi:10.1007/978-3-030-55878-9_6'
apa: Berisha-Gawlowski, Angelina, Caruso, C., & Harteis, C. (2021). The Concept
of a Digital Twin and Its Potential for Learning Organizations. In D. Ifenthaler,
S. Hofhues, M. Egloffstein, & C. Helbig (Eds.), Digital Transformation
of Learning Organizations (pp. 95–114). Springer. https://doi.org/10.1007/978-3-030-55878-9_6
bibtex: '@inbook{Berisha-Gawlowski_Caruso_Harteis_2021, place={Cham}, title={The
Concept of a Digital Twin and Its Potential for Learning Organizations}, DOI={10.1007/978-3-030-55878-9_6},
booktitle={Digital Transformation of Learning Organizations }, publisher={Springer},
author={Berisha-Gawlowski, Angelina and Caruso, Carina and Harteis, Christian},
editor={Ifenthaler, Dirk and Hofhues, Sandra and Egloffstein, Marc and Helbig,
Christian}, year={2021}, pages={95–114} }'
chicago: 'Berisha-Gawlowski, Angelina, Carina Caruso, and Christian Harteis. “The
Concept of a Digital Twin and Its Potential for Learning Organizations.” In Digital
Transformation of Learning Organizations , edited by Dirk Ifenthaler, Sandra
Hofhues, Marc Egloffstein, and Christian Helbig, 95–114. Cham: Springer, 2021.
https://doi.org/10.1007/978-3-030-55878-9_6.'
ieee: 'Angelina Berisha-Gawlowski, C. Caruso, and C. Harteis, “The Concept of a
Digital Twin and Its Potential for Learning Organizations,” in Digital Transformation
of Learning Organizations , D. Ifenthaler, S. Hofhues, M. Egloffstein, and
C. Helbig, Eds. Cham: Springer, 2021, pp. 95–114.'
mla: Berisha-Gawlowski, Angelina, et al. “The Concept of a Digital Twin and Its
Potential for Learning Organizations.” Digital Transformation of Learning Organizations
, edited by Dirk Ifenthaler et al., Springer, 2021, pp. 95–114, doi:10.1007/978-3-030-55878-9_6.
short: 'Angelina Berisha-Gawlowski, C. Caruso, C. Harteis, in: D. Ifenthaler, S.
Hofhues, M. Egloffstein, C. Helbig (Eds.), Digital Transformation of Learning
Organizations , Springer, Cham, 2021, pp. 95–114.'
date_created: 2023-01-09T10:38:42Z
date_updated: 2023-01-09T10:39:26Z
doi: 10.1007/978-3-030-55878-9_6
editor:
- first_name: Dirk
full_name: Ifenthaler, Dirk
last_name: Ifenthaler
- first_name: Sandra
full_name: Hofhues, Sandra
last_name: Hofhues
- first_name: Marc
full_name: Egloffstein, Marc
last_name: Egloffstein
- first_name: Christian
full_name: Helbig, Christian
last_name: Helbig
keyword:
- Digital twin
- Learning organization
- Change
- Team learning
- Professional development
language:
- iso: eng
page: ' 95–114'
place: Cham
publication: 'Digital Transformation of Learning Organizations '
publication_identifier:
eisbn:
- 978-3-030-55878-9
isbn:
- 978-3-030-55877-2
publication_status: published
publisher: Springer
status: public
title: The Concept of a Digital Twin and Its Potential for Learning Organizations
type: book_chapter
user_id: '86519'
year: '2021'
...
---
_id: '35889'
abstract:
- lang: eng
text: Network and service coordination is important to provide modern services consisting
of multiple interconnected components, e.g., in 5G, network function virtualization
(NFV), or cloud and edge computing. In this paper, I outline my dissertation research,
which proposes six approaches to automate such network and service coordination.
All approaches dynamically react to the current demand and optimize coordination
for high service quality and low costs. The approaches range from centralized
to distributed methods and from conventional heuristic algorithms and mixed-integer
linear programs to machine learning approaches using supervised and reinforcement
learning. I briefly discuss their main ideas and advantages over other state-of-the-art
approaches and compare strengths and weaknesses.
author:
- first_name: Stefan Balthasar
full_name: Schneider, Stefan Balthasar
id: '35343'
last_name: Schneider
orcid: 0000-0001-8210-4011
citation:
ama: Schneider SB. Conventional and Machine Learning Approaches for Network and
Service Coordination.; 2021.
apa: Schneider, S. B. (2021). Conventional and Machine Learning Approaches for
Network and Service Coordination.
bibtex: '@book{Schneider_2021, title={Conventional and Machine Learning Approaches
for Network and Service Coordination}, author={Schneider, Stefan Balthasar}, year={2021}
}'
chicago: Schneider, Stefan Balthasar. Conventional and Machine Learning Approaches
for Network and Service Coordination, 2021.
ieee: S. B. Schneider, Conventional and Machine Learning Approaches for Network
and Service Coordination. 2021.
mla: Schneider, Stefan Balthasar. Conventional and Machine Learning Approaches
for Network and Service Coordination. 2021.
short: S.B. Schneider, Conventional and Machine Learning Approaches for Network
and Service Coordination, 2021.
date_created: 2023-01-10T15:08:50Z
date_updated: 2023-01-10T15:09:05Z
ddc:
- '004'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: stschn
date_created: 2023-01-10T15:07:03Z
date_updated: 2023-01-10T15:07:03Z
file_id: '35890'
file_name: main.pdf
file_size: 133340
relation: main_file
file_date_updated: 2023-01-10T15:07:03Z
has_accepted_license: '1'
keyword:
- nfv
- coordination
- machine learning
- reinforcement learning
- phd
- digest
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '4'
name: 'SFB 901 - C: SFB 901 - Project Area C'
- _id: '16'
name: 'SFB 901 - C4: SFB 901 - Subproject C4'
status: public
title: Conventional and Machine Learning Approaches for Network and Service Coordination
type: working_paper
user_id: '35343'
year: '2021'
...
---
_id: '37136'
abstract:
- lang: eng
text: This study examines the relation between voluntary audit and the cost of debt
in private firms. We use a sample of 4,058 small private firms operating in the
period 2006‐2017 that are not subject to mandatory audits. Firms decide for a
voluntary audit of financial statements either because the economic setting in
which they operate effectively forces them to do so (e.g., ownership complexity,
export‐oriented supply chain, subsidiary status) or because firm fundamentals
and/or financial reporting practices limit their access to financial debt, both
reflected in earnings quality. We use these factors to model the decision for
voluntary audit. In the outcome analyses, we find robust evidence that voluntary
audits are associated with higher, rather than lower, interest rate by up to 3.0
percentage points. This effect is present regardless of the perceived audit quality
(Big‐4 vs. non‐Big‐4), but is stronger for non‐Big‐4 audits where auditees have
a stronger position relative to auditors. Audited firms’ earnings are less informative
about future operating performance relative to unaudited counterparts. We conclude
that voluntary audits facilitate access to financial debt for firms with higher
risk that may otherwise have no access to this form of financing. The price paid
is reflected in higher interest rates charged to firms with voluntary audits –
firms with higher information and/or fundamental risk.
author:
- first_name: Riste
full_name: Ichev, Riste
last_name: Ichev
- first_name: Jernej
full_name: Koren, Jernej
last_name: Koren
- first_name: Urska
full_name: Kosi, Urska
id: '54068'
last_name: Kosi
- first_name: Katarina
full_name: Sitar Sustar, Katarina
last_name: Sitar Sustar
- first_name: Aljosa
full_name: Valentincic, Aljosa
last_name: Valentincic
citation:
ama: 'Ichev R, Koren J, Kosi U, Sitar Sustar K, Valentincic A. Cost of Debt for
Private Firms Revisited: Voluntary Audits as a Reflection of Risk.; 2021.'
apa: 'Ichev, R., Koren, J., Kosi, U., Sitar Sustar, K., & Valentincic, A. (2021).
Cost of Debt for Private Firms Revisited: Voluntary Audits as a Reflection
of Risk.'
bibtex: '@book{Ichev_Koren_Kosi_Sitar Sustar_Valentincic_2021, title={Cost of Debt
for Private Firms Revisited: Voluntary Audits as a Reflection of Risk}, author={Ichev,
Riste and Koren, Jernej and Kosi, Urska and Sitar Sustar, Katarina and Valentincic,
Aljosa}, year={2021} }'
chicago: 'Ichev, Riste, Jernej Koren, Urska Kosi, Katarina Sitar Sustar, and Aljosa
Valentincic. Cost of Debt for Private Firms Revisited: Voluntary Audits as
a Reflection of Risk, 2021.'
ieee: 'R. Ichev, J. Koren, U. Kosi, K. Sitar Sustar, and A. Valentincic, Cost
of Debt for Private Firms Revisited: Voluntary Audits as a Reflection of Risk.
2021.'
mla: 'Ichev, Riste, et al. Cost of Debt for Private Firms Revisited: Voluntary
Audits as a Reflection of Risk. 2021.'
short: 'R. Ichev, J. Koren, U. Kosi, K. Sitar Sustar, A. Valentincic, Cost of Debt
for Private Firms Revisited: Voluntary Audits as a Reflection of Risk, 2021.'
date_created: 2023-01-17T15:03:08Z
date_updated: 2023-01-18T13:40:40Z
department:
- _id: '635'
- _id: '186'
- _id: '551'
keyword:
- private firms
- voluntary audit
- cost of debt
- self‐selection bias
- risk
language:
- iso: eng
main_file_link:
- url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3853927
status: public
title: 'Cost of Debt for Private Firms Revisited: Voluntary Audits as a Reflection
of Risk'
type: working_paper
user_id: '88603'
year: '2021'
...
---
_id: '24547'
abstract:
- lang: eng
text: 'Over the last years, several approaches for the data-driven estimation of
expected possession value (EPV) in basketball and association football (soccer)
have been proposed. In this paper, we develop and evaluate PIVOT: the first such
framework for team handball. Accounting for the fast-paced, dynamic nature and
relative data scarcity of hand- ball, we propose a parsimonious end-to-end deep
learning architecture that relies solely on tracking data. This efficient approach
is capable of predicting the probability that a team will score within the near
future given the fine-grained spatio-temporal distribution of all players and
the ball over the last seconds of the game. Our experiments indicate that PIVOT
is able to produce accurate and calibrated probability estimates, even when trained
on a relatively small dataset. We also showcase two interactive applications of
PIVOT for valuing actual and counterfactual player decisions and actions in real-time.'
author:
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
- first_name: Matthew
full_name: Caron, Matthew
id: '60721'
last_name: Caron
- first_name: Michael
full_name: Döring, Michael
last_name: Döring
- first_name: Tim
full_name: Heuwinkel, Tim
last_name: Heuwinkel
- first_name: Jochen
full_name: Baumeister, Jochen
id: '46'
last_name: Baumeister
orcid: 0000-0003-2683-5826
citation:
ama: 'Müller O, Caron M, Döring M, Heuwinkel T, Baumeister J. PIVOT: A Parsimonious
End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking
Data. In: 8th Workshop on Machine Learning and Data Mining for Sports Analytics
(ECML PKDD 2021).'
apa: 'Müller, O., Caron, M., Döring, M., Heuwinkel, T., & Baumeister, J. (n.d.).
PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions
in Handball using Tracking Data. 8th Workshop on Machine Learning and Data
Mining for Sports Analytics (ECML PKDD 2021). European Conference on Machine
Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021),
Online.'
bibtex: '@inproceedings{Müller_Caron_Döring_Heuwinkel_Baumeister, title={PIVOT:
A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball
using Tracking Data}, booktitle={8th Workshop on Machine Learning and Data Mining
for Sports Analytics (ECML PKDD 2021)}, author={Müller, Oliver and Caron, Matthew
and Döring, Michael and Heuwinkel, Tim and Baumeister, Jochen} }'
chicago: 'Müller, Oliver, Matthew Caron, Michael Döring, Tim Heuwinkel, and Jochen
Baumeister. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player
Actions in Handball Using Tracking Data.” In 8th Workshop on Machine Learning
and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.'
ieee: 'O. Müller, M. Caron, M. Döring, T. Heuwinkel, and J. Baumeister, “PIVOT:
A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball
using Tracking Data,” presented at the European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online.'
mla: 'Müller, Oliver, et al. “PIVOT: A Parsimonious End-to-End Learning Framework
for Valuing Player Actions in Handball Using Tracking Data.” 8th Workshop on
Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021).'
short: 'O. Müller, M. Caron, M. Döring, T. Heuwinkel, J. Baumeister, in: 8th Workshop
on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.'
conference:
end_date: 2021-09-17
location: Online
name: European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery (ECML PKDD 2021)
start_date: 2021-09-13
date_created: 2021-09-16T08:33:04Z
date_updated: 2023-02-28T08:58:24Z
department:
- _id: '196'
- _id: '172'
keyword:
- expected possession value
- handball
- tracking data
- time series classification
- deep learning
language:
- iso: eng
main_file_link:
- url: https://dtai.cs.kuleuven.be/events/MLSA21/papers/MLSA21_paper_muller.pdf
publication: 8th Workshop on Machine Learning and Data Mining for Sports Analytics
(ECML PKDD 2021)
publication_status: inpress
status: public
title: 'PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions
in Handball using Tracking Data'
type: conference
user_id: '60721'
year: '2021'
...